Senior AI/ML Engineer: LLM, RAG & End-to-End Deployment

Vizient, Inc.; Centennial (CO); On-site; USD 102,400 - 179,000; Full time

Benefits offered by this job

Incentive eligible

Benefits plan

Job summary

Vizient, Inc. seeks an experienced AI/ML Engineer to design, build, and deploy scalable ML and LLM-based solutions.

You will lead the full AI lifecycle from experimentation through production, delivering both traditional models and RAG-powered systems, while collaborating across teams to meet client needs. You will design RAG pipelines, embedding strategies, and vector search, build agentic workflows, and establish robust MLOps, monitoring, and governance practices.

Qualifications

• 5+ years of relevant experience in ML engineering or applied AI.
• Experience deploying ML models into production environments.
• Experience building LLM-powered applications such as RAG systems.
• Strong Python expertise and production-grade software practices.
• Experience with vector databases, embeddings, and model serving APIs.

Responsibilities

• Lead design, development, and deployment of ML models and LLM-based apps.
• Translate business challenges into scalable AI solutions with KPI alignment.
• Design and implement RAG pipelines and embedding strategies.
• Build agentic workflows and orchestration patterns for LLM systems.
• Own AI/ML solutions end-to-end with high autonomy and governance.
• Evaluate model/LLM performance with automated and human-in-the-loop methods.
• Optimize latency, cost, scalability, and reliability of AI systems.
• Maintain CI/CD, MLOps, data pipelines with validation and lineage.

Skills

Python

ML engineering

LLM systems

RAG pipelines

vector databases

CI/CD

Docker/Kubernetes

LangChain/LlamaIndex

Education

Advanced degree in CS/Engineering/Data Science

Tools

FastAPI

MLflow

LangChain

LlamaIndex

Docker

Kubernetes

Job description

Vizient, Inc. seeks an experienced AI/ML Engineer to design, build, and deploy scalable ML and LLM-based solutions.

You will lead the full AI lifecycle from experimentation through production, delivering both traditional models and RAG-powered systems, while collaborating across teams to meet client needs. You will design RAG pipelines, embedding strategies, and vector search, build agentic workflows, and establish robust MLOps, monitoring, and governance practices.